Hubungan Sumber Daya dan Struktur Birokrasi Protokol Kesehatan Terhadap Persepsi Penurunan Angka Kejadian Covid-19
Bibliographic record
Abstract
The increase in the number of cases of COVID-19 is still happening every day, therefore, a very comprehensive effort is needed in managing the Covid-19 case and ways to break the chain of exposure. Based on data from the Covid-19 Handling Task Force as of March 31, 2022 globally, the total number of Covid-19 infections was 486,661,998 cases,active cases of covid-19 as many as 58,643,881 cases,and the death rate reached 6,161,174 people.This study aims to find out the relationship between resources and bureaucratic structure with the perception of a decrease in the number of Covid-19 cases at Bina Jaya Vocational School in 2022. The design of this study is descriptive analytic, using a cross sectional approach. The sample of this research is 71 respondents at SMK Bina Jaya Palembang, and the sampling technique uses accidental sampling. Resources are urgently needed to support the implementation, such as human resources who are actively involved in the policy process, the availability of facilities to support the policies that have been set, unitary system that gives centralized resources of authority to the national government as well as the bureaucratic structure including the dimensions of fragmentation and SOPs which have a significant influence on the success of policy implementation, such as inter-organizational relations as contingency theory, as case analysis, chart of public sector policy and management subsystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".